Overview

Brought to you by YData

Dataset statistics

Number of variables19
Number of observations451
Missing cells1
Missing cells (%)< 0.1%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory440.7 KiB
Average record size in memory1000.5 B

Variable types

Categorical5
Numeric7
DateTime3
Boolean1
Text3

Alerts

nsfw has constant value "False"Constant
Predicted post score is highly overall correlated with Time Since Post and 4 other fieldsHigh correlation
Time Since Post is highly overall correlated with Predicted post score and 4 other fieldsHigh correlation
engagement_percentage is highly overall correlated with engagement_score and 6 other fieldsHigh correlation
engagement_score is highly overall correlated with engagement_percentage and 6 other fieldsHigh correlation
number of comments is highly overall correlated with engagement_percentage and 6 other fieldsHigh correlation
post id is highly overall correlated with Predicted post score and 8 other fieldsHigh correlation
post score is highly overall correlated with engagement_percentage and 6 other fieldsHigh correlation
post title is highly overall correlated with Predicted post score and 8 other fieldsHigh correlation
post url is highly overall correlated with Predicted post score and 8 other fieldsHigh correlation
posted by is highly overall correlated with Predicted post score and 8 other fieldsHigh correlation
post flag is highly imbalanced (59.9%)Imbalance
comment id has unique valuesUnique
post score has 46 (10.2%) zerosZeros
comment score has 14 (3.1%) zerosZeros
Time Since Post has 22 (4.9%) zerosZeros

Reproduction

Analysis started2025-01-24 20:30:24.181147
Analysis finished2025-01-24 20:30:34.726376
Duration10.55 seconds
Software versionydata-profiling vv4.10.0
Download configurationconfig.json

Variables

post id
Categorical

HIGH CORRELATION 

Distinct20
Distinct (%)4.4%
Missing0
Missing (%)0.0%
Memory size28.3 KiB
1i6ihle
71 
1i8vqlf
50 
1i5j5b4
46 
1i4ulhz
44 
1i6329d
42 
Other values (15)
198 

Length

Max length7
Median length7
Mean length7
Min length7

Characters and Unicode

Total characters3157
Distinct characters35
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique2 ?
Unique (%)0.4%

Sample

1st row1i91b7m
2nd row1i91b7m
3rd row1i91b7m
4th row1i91b7m
5th row1i91b7m

Common Values

ValueCountFrequency (%)
1i6ihle 71
15.7%
1i8vqlf 50
11.1%
1i5j5b4 46
10.2%
1i4ulhz 44
9.8%
1i6329d 42
9.3%
1i55gki 30
 
6.7%
1i91b7m 22
 
4.9%
1i8j7mf 22
 
4.9%
1i6k33h 21
 
4.7%
1i72rjx 19
 
4.2%
Other values (10) 84
18.6%

Length

2025-01-25T02:00:34.866375image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
1i6ihle 71
15.7%
1i8vqlf 50
11.1%
1i5j5b4 46
10.2%
1i4ulhz 44
9.8%
1i6329d 42
9.3%
1i55gki 30
 
6.7%
1i91b7m 22
 
4.9%
1i8j7mf 22
 
4.9%
1i6k33h 21
 
4.7%
1i72rjx 19
 
4.2%
Other values (10) 84
18.6%

Most occurring characters

ValueCountFrequency (%)
i 553
17.5%
1 491
15.6%
5 199
 
6.3%
l 166
 
5.3%
6 160
 
5.1%
h 143
 
4.5%
8 114
 
3.6%
7 94
 
3.0%
4 91
 
2.9%
j 91
 
2.9%
Other values (25) 1055
33.4%

Most occurring categories

ValueCountFrequency (%)
(unknown) 3157
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
i 553
17.5%
1 491
15.6%
5 199
 
6.3%
l 166
 
5.3%
6 160
 
5.1%
h 143
 
4.5%
8 114
 
3.6%
7 94
 
3.0%
4 91
 
2.9%
j 91
 
2.9%
Other values (25) 1055
33.4%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 3157
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
i 553
17.5%
1 491
15.6%
5 199
 
6.3%
l 166
 
5.3%
6 160
 
5.1%
h 143
 
4.5%
8 114
 
3.6%
7 94
 
3.0%
4 91
 
2.9%
j 91
 
2.9%
Other values (25) 1055
33.4%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 3157
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
i 553
17.5%
1 491
15.6%
5 199
 
6.3%
l 166
 
5.3%
6 160
 
5.1%
h 143
 
4.5%
8 114
 
3.6%
7 94
 
3.0%
4 91
 
2.9%
j 91
 
2.9%
Other values (25) 1055
33.4%

post title
Categorical

HIGH CORRELATION 

Distinct20
Distinct (%)4.4%
Missing0
Missing (%)0.0%
Memory size43.4 KiB
Favorite comfort food
71 
What's the weirdest food combination you secretly love?
50 
Vegetarian indian food that is low in gluten
46 
How to consume amla without the sour taste?
44 
Coriander substitute for butter chicken? (US, minimal experience)
42 
Other values (15)
198 

Length

Max length72
Median length60
Mean length41.27051
Min length17

Characters and Unicode

Total characters18613
Distinct characters53
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique2 ?
Unique (%)0.4%

Sample

1st rowWhich type of onions do you like best when cooking indian food?
2nd rowWhich type of onions do you like best when cooking indian food?
3rd rowWhich type of onions do you like best when cooking indian food?
4th rowWhich type of onions do you like best when cooking indian food?
5th rowWhich type of onions do you like best when cooking indian food?

Common Values

ValueCountFrequency (%)
Favorite comfort food 71
15.7%
What's the weirdest food combination you secretly love? 50
11.1%
Vegetarian indian food that is low in gluten 46
10.2%
How to consume amla without the sour taste? 44
9.8%
Coriander substitute for butter chicken? (US, minimal experience) 42
9.3%
Does Indian food mean vegetarian food? 30
 
6.7%
Which type of onions do you like best when cooking indian food? 22
 
4.9%
Hit me with your fave lamb curry recipes 22
 
4.9%
MTR Dosa mix fail 21
 
4.7%
Dishes to pack and take on journeys? 19
 
4.2%
Other values (10) 84
18.6%

Length

2025-01-25T02:00:35.059454image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
food 268
 
8.8%
indian 116
 
3.8%
the 112
 
3.7%
favorite 82
 
2.7%
comfort 82
 
2.7%
vegetarian 76
 
2.5%
to 74
 
2.4%
for 73
 
2.4%
you 72
 
2.4%
that 64
 
2.1%
Other values (99) 2017
66.4%

Most occurring characters

ValueCountFrequency (%)
2674
14.4%
o 1822
 
9.8%
e 1606
 
8.6%
t 1372
 
7.4%
i 1281
 
6.9%
a 1045
 
5.6%
n 1009
 
5.4%
r 868
 
4.7%
s 758
 
4.1%
d 636
 
3.4%
Other values (43) 5542
29.8%

Most occurring categories

ValueCountFrequency (%)
(unknown) 18613
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
2674
14.4%
o 1822
 
9.8%
e 1606
 
8.6%
t 1372
 
7.4%
i 1281
 
6.9%
a 1045
 
5.6%
n 1009
 
5.4%
r 868
 
4.7%
s 758
 
4.1%
d 636
 
3.4%
Other values (43) 5542
29.8%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 18613
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
2674
14.4%
o 1822
 
9.8%
e 1606
 
8.6%
t 1372
 
7.4%
i 1281
 
6.9%
a 1045
 
5.6%
n 1009
 
5.4%
r 868
 
4.7%
s 758
 
4.1%
d 636
 
3.4%
Other values (43) 5542
29.8%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 18613
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
2674
14.4%
o 1822
 
9.8%
e 1606
 
8.6%
t 1372
 
7.4%
i 1281
 
6.9%
a 1045
 
5.6%
n 1009
 
5.4%
r 868
 
4.7%
s 758
 
4.1%
d 636
 
3.4%
Other values (43) 5542
29.8%

post url
Categorical

HIGH CORRELATION 

Distinct20
Distinct (%)4.4%
Missing0
Missing (%)0.0%
Memory size64.8 KiB
https://www.reddit.com/r/IndianFood/comments/1i6ihle/favorite_comfort_food/
71 
https://www.reddit.com/r/IndianFood/comments/1i8vqlf/whats_the_weirdest_food_combination_you_secretly/
50 
https://www.reddit.com/r/IndianFood/comments/1i5j5b4/vegetarian_indian_food_that_is_low_in_gluten/
46 
https://www.reddit.com/r/IndianFood/comments/1i4ulhz/how_to_consume_amla_without_the_sour_taste/
44 
https://www.reddit.com/r/IndianFood/comments/1i6329d/coriander_substitute_for_butter_chicken_us/
42 
Other values (15)
198 

Length

Max length103
Median length101
Mean length89.902439
Min length71

Characters and Unicode

Total characters40546
Distinct characters42
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique2 ?
Unique (%)0.4%

Sample

1st rowhttps://www.reddit.com/r/IndianFood/comments/1i91b7m/which_type_of_onions_do_you_like_best_when/
2nd rowhttps://www.reddit.com/r/IndianFood/comments/1i91b7m/which_type_of_onions_do_you_like_best_when/
3rd rowhttps://www.reddit.com/r/IndianFood/comments/1i91b7m/which_type_of_onions_do_you_like_best_when/
4th rowhttps://www.reddit.com/r/IndianFood/comments/1i91b7m/which_type_of_onions_do_you_like_best_when/
5th rowhttps://www.reddit.com/r/IndianFood/comments/1i91b7m/which_type_of_onions_do_you_like_best_when/

Common Values

ValueCountFrequency (%)
https://www.reddit.com/r/IndianFood/comments/1i6ihle/favorite_comfort_food/ 71
15.7%
https://www.reddit.com/r/IndianFood/comments/1i8vqlf/whats_the_weirdest_food_combination_you_secretly/ 50
11.1%
https://www.reddit.com/r/IndianFood/comments/1i5j5b4/vegetarian_indian_food_that_is_low_in_gluten/ 46
10.2%
https://www.reddit.com/r/IndianFood/comments/1i4ulhz/how_to_consume_amla_without_the_sour_taste/ 44
9.8%
https://www.reddit.com/r/IndianFood/comments/1i6329d/coriander_substitute_for_butter_chicken_us/ 42
9.3%
https://www.reddit.com/r/IndianFood/comments/1i55gki/does_indian_food_mean_vegetarian_food/ 30
 
6.7%
https://www.reddit.com/r/IndianFood/comments/1i91b7m/which_type_of_onions_do_you_like_best_when/ 22
 
4.9%
https://www.reddit.com/r/IndianFood/comments/1i8j7mf/hit_me_with_your_fave_lamb_curry_recipes/ 22
 
4.9%
https://www.reddit.com/r/IndianFood/comments/1i6k33h/mtr_dosa_mix_fail/ 21
 
4.7%
https://www.reddit.com/r/IndianFood/comments/1i72rjx/dishes_to_pack_and_take_on_journeys/ 19
 
4.2%
Other values (10) 84
18.6%

Length

2025-01-25T02:00:35.288081image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
https://www.reddit.com/r/indianfood/comments/1i6ihle/favorite_comfort_food 71
15.7%
https://www.reddit.com/r/indianfood/comments/1i8vqlf/whats_the_weirdest_food_combination_you_secretly 50
11.1%
https://www.reddit.com/r/indianfood/comments/1i5j5b4/vegetarian_indian_food_that_is_low_in_gluten 46
10.2%
https://www.reddit.com/r/indianfood/comments/1i4ulhz/how_to_consume_amla_without_the_sour_taste 44
9.8%
https://www.reddit.com/r/indianfood/comments/1i6329d/coriander_substitute_for_butter_chicken_us 42
9.3%
https://www.reddit.com/r/indianfood/comments/1i55gki/does_indian_food_mean_vegetarian_food 30
 
6.7%
https://www.reddit.com/r/indianfood/comments/1i91b7m/which_type_of_onions_do_you_like_best_when 22
 
4.9%
https://www.reddit.com/r/indianfood/comments/1i8j7mf/hit_me_with_your_fave_lamb_curry_recipes 22
 
4.9%
https://www.reddit.com/r/indianfood/comments/1i6k33h/mtr_dosa_mix_fail 21
 
4.7%
https://www.reddit.com/r/indianfood/comments/1i72rjx/dishes_to_pack_and_take_on_journeys 19
 
4.2%
Other values (10) 84
18.6%

Most occurring characters

ValueCountFrequency (%)
/ 3608
 
8.9%
o 3500
 
8.6%
t 3196
 
7.9%
i 2575
 
6.4%
d 2493
 
6.1%
_ 2330
 
5.7%
e 2317
 
5.7%
n 2202
 
5.4%
m 1746
 
4.3%
r 1741
 
4.3%
Other values (32) 14838
36.6%

Most occurring categories

ValueCountFrequency (%)
(unknown) 40546
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
/ 3608
 
8.9%
o 3500
 
8.6%
t 3196
 
7.9%
i 2575
 
6.4%
d 2493
 
6.1%
_ 2330
 
5.7%
e 2317
 
5.7%
n 2202
 
5.4%
m 1746
 
4.3%
r 1741
 
4.3%
Other values (32) 14838
36.6%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 40546
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
/ 3608
 
8.9%
o 3500
 
8.6%
t 3196
 
7.9%
i 2575
 
6.4%
d 2493
 
6.1%
_ 2330
 
5.7%
e 2317
 
5.7%
n 2202
 
5.4%
m 1746
 
4.3%
r 1741
 
4.3%
Other values (32) 14838
36.6%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 40546
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
/ 3608
 
8.9%
o 3500
 
8.6%
t 3196
 
7.9%
i 2575
 
6.4%
d 2493
 
6.1%
_ 2330
 
5.7%
e 2317
 
5.7%
n 2202
 
5.4%
m 1746
 
4.3%
r 1741
 
4.3%
Other values (32) 14838
36.6%

post score
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct10
Distinct (%)2.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean8.7117517
Minimum0
Maximum20
Zeros46
Zeros (%)10.2%
Negative0
Negative (%)0.0%
Memory size3.6 KiB
2025-01-25T02:00:35.596164image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q14
median7
Q311
95-th percentile20
Maximum20
Range20
Interquartile range (IQR)7

Descriptive statistics

Standard deviation6.3656243
Coefficient of variation (CV)0.73069396
Kurtosis-0.71431883
Mean8.7117517
Median Absolute Deviation (MAD)4
Skewness0.57545307
Sum3929
Variance40.521173
MonotonicityNot monotonic
2025-01-25T02:00:35.787733image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=10)
ValueCountFrequency (%)
11 88
19.5%
20 71
15.7%
7 63
14.0%
4 57
12.6%
0 46
10.2%
8 44
9.8%
5 22
 
4.9%
3 22
 
4.9%
1 20
 
4.4%
18 18
 
4.0%
ValueCountFrequency (%)
0 46
10.2%
1 20
 
4.4%
3 22
 
4.9%
4 57
12.6%
5 22
 
4.9%
7 63
14.0%
8 44
9.8%
11 88
19.5%
18 18
 
4.0%
20 71
15.7%
ValueCountFrequency (%)
20 71
15.7%
18 18
 
4.0%
11 88
19.5%
8 44
9.8%
7 63
14.0%
5 22
 
4.9%
4 57
12.6%
3 22
 
4.9%
1 20
 
4.4%
0 46
10.2%

posted by
Categorical

HIGH CORRELATION 

Distinct20
Distinct (%)4.4%
Missing0
Missing (%)0.0%
Memory size31.5 KiB
jonstonprods
71 
Nuclear_FartBlasts
50 
Thin_Letterhead_9195
46 
cool_cat1549
44 
ThadTheHusky
42 
Other values (15)
198 

Length

Max length20
Median length18
Mean length14.141907
Min length7

Characters and Unicode

Total characters6378
Distinct characters48
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique2 ?
Unique (%)0.4%

Sample

1st rowkrishnab75
2nd rowkrishnab75
3rd rowkrishnab75
4th rowkrishnab75
5th rowkrishnab75

Common Values

ValueCountFrequency (%)
jonstonprods 71
15.7%
Nuclear_FartBlasts 50
11.1%
Thin_Letterhead_9195 46
10.2%
cool_cat1549 44
9.8%
ThadTheHusky 42
9.3%
iMarcoPolo007 30
 
6.7%
krishnab75 22
 
4.9%
challawarra 22
 
4.9%
zoechowber 21
 
4.7%
ECrispy 19
 
4.2%
Other values (10) 84
18.6%

Length

2025-01-25T02:00:36.082558image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
jonstonprods 71
15.7%
nuclear_fartblasts 50
11.1%
thin_letterhead_9195 46
10.2%
cool_cat1549 44
9.8%
thadthehusky 42
9.3%
imarcopolo007 30
 
6.7%
krishnab75 22
 
4.9%
challawarra 22
 
4.9%
zoechowber 21
 
4.7%
ecrispy 19
 
4.2%
Other values (10) 84
18.6%

Most occurring characters

ValueCountFrequency (%)
a 536
 
8.4%
o 506
 
7.9%
r 411
 
6.4%
e 378
 
5.9%
s 372
 
5.8%
t 355
 
5.6%
n 323
 
5.1%
h 314
 
4.9%
c 264
 
4.1%
l 261
 
4.1%
Other values (38) 2658
41.7%

Most occurring categories

ValueCountFrequency (%)
(unknown) 6378
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
a 536
 
8.4%
o 506
 
7.9%
r 411
 
6.4%
e 378
 
5.9%
s 372
 
5.8%
t 355
 
5.6%
n 323
 
5.1%
h 314
 
4.9%
c 264
 
4.1%
l 261
 
4.1%
Other values (38) 2658
41.7%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 6378
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
a 536
 
8.4%
o 506
 
7.9%
r 411
 
6.4%
e 378
 
5.9%
s 372
 
5.8%
t 355
 
5.6%
n 323
 
5.1%
h 314
 
4.9%
c 264
 
4.1%
l 261
 
4.1%
Other values (38) 2658
41.7%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 6378
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
a 536
 
8.4%
o 506
 
7.9%
r 411
 
6.4%
e 378
 
5.9%
s 372
 
5.8%
t 355
 
5.6%
n 323
 
5.1%
h 314
 
4.9%
c 264
 
4.1%
l 261
 
4.1%
Other values (38) 2658
41.7%
Distinct20
Distinct (%)4.4%
Missing0
Missing (%)0.0%
Memory size3.6 KiB
Minimum2025-01-19 09:11:20
Maximum2025-01-24 17:48:16
2025-01-25T02:00:36.261744image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2025-01-25T02:00:36.443088image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=20)

nsfw
Boolean

CONSTANT 

Distinct1
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Memory size579.0 B
False
451 
ValueCountFrequency (%)
False 451
100.0%
2025-01-25T02:00:36.620840image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

number of comments
Real number (ℝ)

HIGH CORRELATION 

Distinct16
Distinct (%)3.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean38.055432
Minimum1
Maximum72
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size3.6 KiB
2025-01-25T02:00:36.803286image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile8
Q121
median42
Q350
95-th percentile72
Maximum72
Range71
Interquartile range (IQR)29

Descriptive statistics

Standard deviation19.69713
Coefficient of variation (CV)0.51759049
Kurtosis-0.83021016
Mean38.055432
Median Absolute Deviation (MAD)19
Skewness0.27416626
Sum17163
Variance387.97692
MonotonicityNot monotonic
2025-01-25T02:00:37.050265image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=16)
ValueCountFrequency (%)
72 71
15.7%
50 50
11.1%
45 46
10.2%
23 44
9.8%
44 44
9.8%
42 42
9.3%
18 35
7.8%
30 30
6.7%
21 21
 
4.7%
19 19
 
4.2%
Other values (6) 49
10.9%
ValueCountFrequency (%)
1 2
 
0.4%
4 8
 
1.8%
7 7
 
1.6%
8 8
 
1.8%
11 11
 
2.4%
13 13
 
2.9%
18 35
7.8%
19 19
4.2%
21 21
4.7%
23 44
9.8%
ValueCountFrequency (%)
72 71
15.7%
50 50
11.1%
45 46
10.2%
44 44
9.8%
42 42
9.3%
30 30
6.7%
23 44
9.8%
21 21
 
4.7%
19 19
 
4.2%
18 35
7.8%

comment id
Text

UNIQUE 

Distinct451
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size28.3 KiB
2025-01-25T02:00:37.467542image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length7
Median length7
Mean length7
Min length7

Characters and Unicode

Total characters3157
Distinct characters36
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique451 ?
Unique (%)100.0%

Sample

1st rowm8yb17e
2nd rowm8yewgd
3rd rowm8y8ia0
4th rowm8y5e3c
5th rowm8ycf4l
ValueCountFrequency (%)
m8yb17e 1
 
0.2%
m8v1mnr 1
 
0.2%
m8y8ia0 1
 
0.2%
m8y5e3c 1
 
0.2%
m8ycf4l 1
 
0.2%
m8yew7r 1
 
0.2%
m8yhnyx 1
 
0.2%
m8ym7vj 1
 
0.2%
m8y4dyq 1
 
0.2%
m8ya0io 1
 
0.2%
Other values (441) 441
97.8%
2025-01-25T02:00:38.188622image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
m 514
 
16.3%
8 479
 
15.2%
x 93
 
2.9%
y 91
 
2.9%
0 90
 
2.9%
d 84
 
2.7%
7 83
 
2.6%
u 80
 
2.5%
b 73
 
2.3%
5 72
 
2.3%
Other values (26) 1498
47.5%

Most occurring categories

ValueCountFrequency (%)
(unknown) 3157
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
m 514
 
16.3%
8 479
 
15.2%
x 93
 
2.9%
y 91
 
2.9%
0 90
 
2.9%
d 84
 
2.7%
7 83
 
2.6%
u 80
 
2.5%
b 73
 
2.3%
5 72
 
2.3%
Other values (26) 1498
47.5%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 3157
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
m 514
 
16.3%
8 479
 
15.2%
x 93
 
2.9%
y 91
 
2.9%
0 90
 
2.9%
d 84
 
2.7%
7 83
 
2.6%
u 80
 
2.5%
b 73
 
2.3%
5 72
 
2.3%
Other values (26) 1498
47.5%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 3157
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
m 514
 
16.3%
8 479
 
15.2%
x 93
 
2.9%
y 91
 
2.9%
0 90
 
2.9%
d 84
 
2.7%
7 83
 
2.6%
u 80
 
2.5%
b 73
 
2.3%
5 72
 
2.3%
Other values (26) 1498
47.5%
Distinct254
Distinct (%)56.4%
Missing1
Missing (%)0.2%
Memory size31.0 KiB
2025-01-25T02:00:38.566170image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length20
Median length15
Mean length13.257778
Min length4

Characters and Unicode

Total characters5966
Distinct characters62
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique172 ?
Unique (%)38.2%

Sample

1st rowMrMcgoomom
2nd rowcymshah
3rd rowNo-Record3007
4th rowshay7700
5th rowMattSk87
ValueCountFrequency (%)
cool_cat1549 13
 
2.9%
anatolian-shepherd-1 11
 
2.4%
imarcopolo007 10
 
2.2%
oarmash 10
 
2.2%
56kandfalling 8
 
1.8%
hot_king1901 7
 
1.6%
jonstonprods 7
 
1.6%
adeptnessmain4170 6
 
1.3%
reasonable_war5271 6
 
1.3%
dogil_saram 6
 
1.3%
Other values (244) 366
81.3%
2025-01-25T02:00:39.173044image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
a 505
 
8.5%
e 422
 
7.1%
o 359
 
6.0%
r 326
 
5.5%
n 321
 
5.4%
i 321
 
5.4%
t 283
 
4.7%
s 247
 
4.1%
l 221
 
3.7%
u 161
 
2.7%
Other values (52) 2800
46.9%

Most occurring categories

ValueCountFrequency (%)
(unknown) 5966
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
a 505
 
8.5%
e 422
 
7.1%
o 359
 
6.0%
r 326
 
5.5%
n 321
 
5.4%
i 321
 
5.4%
t 283
 
4.7%
s 247
 
4.1%
l 221
 
3.7%
u 161
 
2.7%
Other values (52) 2800
46.9%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 5966
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
a 505
 
8.5%
e 422
 
7.1%
o 359
 
6.0%
r 326
 
5.5%
n 321
 
5.4%
i 321
 
5.4%
t 283
 
4.7%
s 247
 
4.1%
l 221
 
3.7%
u 161
 
2.7%
Other values (52) 2800
46.9%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 5966
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
a 505
 
8.5%
e 422
 
7.1%
o 359
 
6.0%
r 326
 
5.5%
n 321
 
5.4%
i 321
 
5.4%
t 283
 
4.7%
s 247
 
4.1%
l 221
 
3.7%
u 161
 
2.7%
Other values (52) 2800
46.9%

comment score
Real number (ℝ)

ZEROS 

Distinct29
Distinct (%)6.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2.5321508
Minimum-10
Maximum73
Zeros14
Zeros (%)3.1%
Negative11
Negative (%)2.4%
Memory size3.6 KiB
2025-01-25T02:00:39.388548image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum-10
5-th percentile0
Q11
median2
Q32.5
95-th percentile8
Maximum73
Range83
Interquartile range (IQR)1.5

Descriptive statistics

Standard deviation4.7338694
Coefficient of variation (CV)1.8695053
Kurtosis112.9944
Mean2.5321508
Median Absolute Deviation (MAD)1
Skewness8.4053166
Sum1142
Variance22.40952
MonotonicityNot monotonic
2025-01-25T02:00:39.596673image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=29)
ValueCountFrequency (%)
1 197
43.7%
2 116
25.7%
3 40
 
8.9%
5 15
 
3.3%
0 14
 
3.1%
4 12
 
2.7%
7 10
 
2.2%
6 9
 
2.0%
8 7
 
1.6%
9 4
 
0.9%
Other values (19) 27
 
6.0%
ValueCountFrequency (%)
-10 2
 
0.4%
-8 1
 
0.2%
-7 1
 
0.2%
-6 1
 
0.2%
-4 1
 
0.2%
-3 1
 
0.2%
-2 1
 
0.2%
-1 3
 
0.7%
0 14
 
3.1%
1 197
43.7%
ValueCountFrequency (%)
73 1
0.2%
30 1
0.2%
25 1
0.2%
18 2
0.4%
17 1
0.2%
16 1
0.2%
15 2
0.4%
13 2
0.4%
12 1
0.2%
11 2
0.4%
Distinct450
Distinct (%)99.8%
Missing0
Missing (%)0.0%
Memory size145.2 KiB
2025-01-25T02:00:40.083109image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length2053
Median length295
Mean length171.36142
Min length2

Characters and Unicode

Total characters77284
Distinct characters112
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique449 ?
Unique (%)99.6%

Sample

1st rowRed. Always. Thankfully they are now more widely available in the US. A few years ago I struggled to find red onions, cilantro and green chillies n grocery stores.
2nd rowDepends on what I'm making. Tomato-onion gravies (pav bhaji, chole chana, etc.) Use almost always red onions or whatever is on hand. For biryani/pulao, shallots. In salads, white or red onions. Onion pakora - red or large yellow onions
3rd rowAlso in America. Red onions. White ones don’t hold the flavour and nobody likes the white ones , even in salads.
4th rowI’m Gujarati in America. We use yellow onions in cooking and a sweet white onion for salads
5th rowSmall red onions or shallots. Red ones are much cheaper.
ValueCountFrequency (%)
the 409
 
3.1%
and 364
 
2.8%
a 280
 
2.1%
to 251
 
1.9%
i 237
 
1.8%
you 212
 
1.6%
in 207
 
1.6%
of 207
 
1.6%
it 205
 
1.6%
is 193
 
1.5%
Other values (2777) 10644
80.6%
2025-01-25T02:00:40.804595image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
12674
16.4%
e 6414
 
8.3%
a 5625
 
7.3%
t 5225
 
6.8%
o 4592
 
5.9%
i 4228
 
5.5%
n 3918
 
5.1%
s 3670
 
4.7%
r 3491
 
4.5%
h 2617
 
3.4%
Other values (102) 24830
32.1%

Most occurring categories

ValueCountFrequency (%)
(unknown) 77284
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
12674
16.4%
e 6414
 
8.3%
a 5625
 
7.3%
t 5225
 
6.8%
o 4592
 
5.9%
i 4228
 
5.5%
n 3918
 
5.1%
s 3670
 
4.7%
r 3491
 
4.5%
h 2617
 
3.4%
Other values (102) 24830
32.1%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 77284
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
12674
16.4%
e 6414
 
8.3%
a 5625
 
7.3%
t 5225
 
6.8%
o 4592
 
5.9%
i 4228
 
5.5%
n 3918
 
5.1%
s 3670
 
4.7%
r 3491
 
4.5%
h 2617
 
3.4%
Other values (102) 24830
32.1%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 77284
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
12674
16.4%
e 6414
 
8.3%
a 5625
 
7.3%
t 5225
 
6.8%
o 4592
 
5.9%
i 4228
 
5.5%
n 3918
 
5.1%
s 3670
 
4.7%
r 3491
 
4.5%
h 2617
 
3.4%
Other values (102) 24830
32.1%
Distinct450
Distinct (%)99.8%
Missing0
Missing (%)0.0%
Memory size3.6 KiB
Minimum2025-01-19 09:17:58
Maximum2025-01-24 20:09:05
2025-01-25T02:00:41.073495image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2025-01-25T02:00:41.316448image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
Distinct20
Distinct (%)4.4%
Missing0
Missing (%)0.0%
Memory size3.6 KiB
Minimum2025-01-19 09:11:20
Maximum2025-01-24 17:48:16
2025-01-25T02:00:41.499700image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2025-01-25T02:00:41.674199image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=20)

post flag
Categorical

IMBALANCE 

Distinct4
Distinct (%)0.9%
Missing0
Missing (%)0.0%
Memory size33.3 KiB
Only Text(No spam)
378 
Too Short Message
48 
Promotional content and link
 
24
Removed or Deleted Post
 
1

Length

Max length28
Median length18
Mean length18.436807
Min length17

Characters and Unicode

Total characters8315
Distinct characters30
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1 ?
Unique (%)0.2%

Sample

1st rowOnly Text(No spam)
2nd rowOnly Text(No spam)
3rd rowOnly Text(No spam)
4th rowOnly Text(No spam)
5th rowOnly Text(No spam)

Common Values

ValueCountFrequency (%)
Only Text(No spam) 378
83.8%
Too Short Message 48
 
10.6%
Promotional content and link 24
 
5.3%
Removed or Deleted Post 1
 
0.2%

Length

2025-01-25T02:00:41.882496image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-01-25T02:00:42.035778image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
only 378
27.4%
text(no 378
27.4%
spam 378
27.4%
too 48
 
3.5%
short 48
 
3.5%
message 48
 
3.5%
promotional 24
 
1.7%
content 24
 
1.7%
and 24
 
1.7%
link 24
 
1.7%
Other values (4) 4
 
0.3%

Most occurring characters

ValueCountFrequency (%)
927
 
11.1%
o 621
 
7.5%
e 503
 
6.0%
t 500
 
6.0%
n 498
 
6.0%
s 475
 
5.7%
a 474
 
5.7%
l 427
 
5.1%
T 426
 
5.1%
m 403
 
4.8%
Other values (20) 3061
36.8%

Most occurring categories

ValueCountFrequency (%)
(unknown) 8315
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
927
 
11.1%
o 621
 
7.5%
e 503
 
6.0%
t 500
 
6.0%
n 498
 
6.0%
s 475
 
5.7%
a 474
 
5.7%
l 427
 
5.1%
T 426
 
5.1%
m 403
 
4.8%
Other values (20) 3061
36.8%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 8315
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
927
 
11.1%
o 621
 
7.5%
e 503
 
6.0%
t 500
 
6.0%
n 498
 
6.0%
s 475
 
5.7%
a 474
 
5.7%
l 427
 
5.1%
T 426
 
5.1%
m 403
 
4.8%
Other values (20) 3061
36.8%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 8315
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
927
 
11.1%
o 621
 
7.5%
e 503
 
6.0%
t 500
 
6.0%
n 498
 
6.0%
s 475
 
5.7%
a 474
 
5.7%
l 427
 
5.1%
T 426
 
5.1%
m 403
 
4.8%
Other values (20) 3061
36.8%

engagement_score
Real number (ℝ)

HIGH CORRELATION 

Distinct18
Distinct (%)4.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean65.7949
Minimum1.5
Maximum128
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size3.6 KiB
2025-01-25T02:00:42.405696image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1.5
5-th percentile13
Q137.5
median74
Q382
95-th percentile128
Maximum128
Range126.5
Interquartile range (IQR)44.5

Descriptive statistics

Standard deviation34.691747
Coefficient of variation (CV)0.52727106
Kurtosis-0.65889395
Mean65.7949
Median Absolute Deviation (MAD)29
Skewness0.40211178
Sum29673.5
Variance1203.5173
MonotonicityNot monotonic
2025-01-25T02:00:42.588979image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=18)
ValueCountFrequency (%)
74 86
19.1%
128 71
15.7%
82 50
11.1%
45 48
10.6%
78.5 46
10.2%
39.5 22
 
4.9%
37.5 22
 
4.9%
35.5 21
 
4.7%
32.5 19
 
4.2%
31 17
 
3.8%
Other values (8) 49
10.9%
ValueCountFrequency (%)
1.5 1
 
0.2%
2.5 1
 
0.2%
6 4
 
0.9%
7 4
 
0.9%
11.5 7
 
1.6%
13 8
1.8%
16.5 11
2.4%
26.5 13
2.9%
31 17
3.8%
32.5 19
4.2%
ValueCountFrequency (%)
128 71
15.7%
82 50
11.1%
78.5 46
10.2%
74 86
19.1%
45 48
10.6%
39.5 22
 
4.9%
37.5 22
 
4.9%
35.5 21
 
4.7%
32.5 19
 
4.2%
31 17
 
3.8%

engagement_percentage
Real number (ℝ)

HIGH CORRELATION 

Distinct18
Distinct (%)4.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.51402266
Minimum0.01171875
Maximum1
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size3.6 KiB
2025-01-25T02:00:42.773318image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0.01171875
5-th percentile0.1015625
Q10.29296875
median0.578125
Q30.640625
95-th percentile1
Maximum1
Range0.98828125
Interquartile range (IQR)0.34765625

Descriptive statistics

Standard deviation0.27102927
Coefficient of variation (CV)0.52727106
Kurtosis-0.65889395
Mean0.51402266
Median Absolute Deviation (MAD)0.2265625
Skewness0.40211178
Sum231.82422
Variance0.073456866
MonotonicityNot monotonic
2025-01-25T02:00:42.965038image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=18)
ValueCountFrequency (%)
0.578125 86
19.1%
1 71
15.7%
0.640625 50
11.1%
0.3515625 48
10.6%
0.61328125 46
10.2%
0.30859375 22
 
4.9%
0.29296875 22
 
4.9%
0.27734375 21
 
4.7%
0.25390625 19
 
4.2%
0.2421875 17
 
3.8%
Other values (8) 49
10.9%
ValueCountFrequency (%)
0.01171875 1
 
0.2%
0.01953125 1
 
0.2%
0.046875 4
 
0.9%
0.0546875 4
 
0.9%
0.08984375 7
 
1.6%
0.1015625 8
1.8%
0.12890625 11
2.4%
0.20703125 13
2.9%
0.2421875 17
3.8%
0.25390625 19
4.2%
ValueCountFrequency (%)
1 71
15.7%
0.640625 50
11.1%
0.61328125 46
10.2%
0.578125 86
19.1%
0.3515625 48
10.6%
0.30859375 22
 
4.9%
0.29296875 22
 
4.9%
0.27734375 21
 
4.7%
0.25390625 19
 
4.2%
0.2421875 17
 
3.8%

Time Since Post
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct20
Distinct (%)4.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean74.239659
Minimum0
Maximum128.61556
Zeros22
Zeros (%)4.9%
Negative0
Negative (%)0.0%
Memory size3.6 KiB
2025-01-25T02:00:43.164536image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile2.4341667
Q124.641389
median76.401389
Q3108.28833
95-th percentile128.61556
Maximum128.61556
Range128.61556
Interquartile range (IQR)83.646944

Descriptive statistics

Standard deviation43.231187
Coefficient of variation (CV)0.58231931
Kurtosis-1.04542
Mean74.239659
Median Absolute Deviation (MAD)31.886944
Skewness-0.54054934
Sum33482.086
Variance1868.9355
MonotonicityIncreasing
2025-01-25T02:00:43.357514image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=20)
ValueCountFrequency (%)
76.40138889 71
15.7%
4.024722222 50
11.1%
108.2883333 46
10.2%
128.6155556 44
9.8%
91.34388889 42
9.3%
119.1627778 30
 
6.7%
0 22
 
4.9%
16.85777778 22
 
4.9%
75.08416667 21
 
4.7%
61.53472222 19
 
4.2%
Other values (10) 84
18.6%
ValueCountFrequency (%)
0 22
 
4.9%
2.434166667 4
 
0.9%
4.024722222 50
11.1%
16.85777778 22
 
4.9%
24.64138889 17
 
3.8%
27.96166667 4
 
0.9%
61.53472222 19
 
4.2%
75.08416667 21
 
4.7%
76.40138889 71
15.7%
78.44916667 11
 
2.4%
ValueCountFrequency (%)
128.6155556 44
9.8%
122.2505556 1
 
0.2%
122.0025 8
 
1.8%
121.4097222 18
 
4.0%
119.1627778 30
6.7%
108.2883333 46
10.2%
95.9425 13
 
2.9%
94.96722222 7
 
1.6%
91.34388889 42
9.3%
88.7825 1
 
0.2%

Predicted post score
Real number (ℝ)

HIGH CORRELATION 

Distinct20
Distinct (%)4.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean8.7253662
Minimum7.0458641
Maximum10.080316
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size3.6 KiB
2025-01-25T02:00:43.526243image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum7.0458641
5-th percentile7.0937846
Q17.5463001
median8.7162463
Q39.5255847
95-th percentile10.080316
Maximum10.080316
Range3.0344522
Interquartile range (IQR)1.9792846

Descriptive statistics

Standard deviation1.015219
Coefficient of variation (CV)0.1163526
Kurtosis-1.1075259
Mean8.7253662
Median Absolute Deviation (MAD)0.80933837
Skewness-0.41644853
Sum3935.1401
Variance1.0306696
MonotonicityIncreasing
2025-01-25T02:00:43.722846image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=20)
ValueCountFrequency (%)
8.716246294 71
15.7%
7.12527313 50
11.1%
9.525584669 46
10.2%
10.08031628 44
9.8%
9.086571581 42
9.3%
9.818441099 30
 
6.7%
7.045864067 22
 
4.9%
7.384499073 22
 
4.9%
8.684334127 21
 
4.7%
8.362779448 19
 
4.2%
Other values (10) 84
18.6%
ValueCountFrequency (%)
7.045864067 22
 
4.9%
7.093784559 4
 
0.9%
7.12527313 50
11.1%
7.384499073 22
 
4.9%
7.546300068 17
 
3.8%
7.616394207 4
 
0.9%
8.362779448 19
 
4.2%
8.684334127 21
 
4.7%
8.716246294 71
15.7%
8.766090553 11
 
2.4%
ValueCountFrequency (%)
10.08031628 44
9.8%
9.903226485 1
 
0.2%
9.896388318 8
 
1.8%
9.880066285 18
 
4.0%
9.818441099 30
6.7%
9.525584669 46
10.2%
9.203676286 13
 
2.9%
9.178715166 7
 
1.6%
9.086571581 42
9.3%
9.021992524 1
 
0.2%

Interactions

2025-01-25T02:00:32.483104image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2025-01-25T02:00:25.309766image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2025-01-25T02:00:26.483037image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2025-01-25T02:00:27.742826image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2025-01-25T02:00:28.812286image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2025-01-25T02:00:30.067454image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2025-01-25T02:00:31.204898image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2025-01-25T02:00:32.942452image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2025-01-25T02:00:25.511490image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2025-01-25T02:00:26.651804image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2025-01-25T02:00:27.883450image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2025-01-25T02:00:28.965091image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2025-01-25T02:00:30.253413image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2025-01-25T02:00:31.411868image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2025-01-25T02:00:33.142807image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2025-01-25T02:00:25.644541image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2025-01-25T02:00:26.851888image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2025-01-25T02:00:28.067837image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2025-01-25T02:00:29.205540image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2025-01-25T02:00:30.451584image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2025-01-25T02:00:31.565167image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2025-01-25T02:00:33.301845image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2025-01-25T02:00:25.727057image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2025-01-25T02:00:27.034601image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2025-01-25T02:00:28.220276image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2025-01-25T02:00:29.342576image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2025-01-25T02:00:30.580450image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2025-01-25T02:00:31.717656image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2025-01-25T02:00:33.460701image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2025-01-25T02:00:25.883407image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2025-01-25T02:00:27.204756image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2025-01-25T02:00:28.357343image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2025-01-25T02:00:29.526771image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2025-01-25T02:00:30.742719image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2025-01-25T02:00:31.889901image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2025-01-25T02:00:33.640831image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2025-01-25T02:00:26.050324image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2025-01-25T02:00:27.389463image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2025-01-25T02:00:28.518215image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2025-01-25T02:00:29.726655image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2025-01-25T02:00:30.867702image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2025-01-25T02:00:32.115301image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2025-01-25T02:00:33.837874image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2025-01-25T02:00:26.274557image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2025-01-25T02:00:27.573673image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2025-01-25T02:00:28.658967image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2025-01-25T02:00:29.900564image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2025-01-25T02:00:31.035452image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2025-01-25T02:00:32.274997image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2025-01-25T02:00:43.867219image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Predicted post scoreTime Since Postcomment scoreengagement_percentageengagement_scorenumber of commentspost flagpost idpost scorepost titlepost urlposted by
Predicted post score1.0001.000-0.072-0.020-0.020-0.0630.1110.9860.1890.9860.9860.986
Time Since Post1.0001.000-0.072-0.020-0.020-0.0630.1240.9870.1890.9870.9870.987
comment score-0.072-0.0721.0000.1070.1070.0570.0000.0000.1610.0000.0000.000
engagement_percentage-0.020-0.0200.1071.0001.0000.9810.1040.9850.7760.9850.9850.985
engagement_score-0.020-0.0200.1071.0001.0000.9810.1040.9850.7760.9850.9850.985
number of comments-0.063-0.0630.0570.9810.9811.0000.0740.9860.6950.9860.9860.986
post flag0.1110.1240.0000.1040.1040.0741.0000.1300.1110.1300.1300.130
post id0.9860.9870.0000.9850.9850.9860.1301.0000.9851.0001.0001.000
post score0.1890.1890.1610.7760.7760.6950.1110.9851.0000.9850.9850.985
post title0.9860.9870.0000.9850.9850.9860.1301.0000.9851.0001.0001.000
post url0.9860.9870.0000.9850.9850.9860.1301.0000.9851.0001.0001.000
posted by0.9860.9870.0000.9850.9850.9860.1301.0000.9851.0001.0001.000

Missing values

2025-01-25T02:00:34.082340image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2025-01-25T02:00:34.566458image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.

Sample

post idpost titlepost urlpost scoreposted bypost datensfwnumber of commentscomment idcomment authorcomment scorecomment bodycomment dateparsed post datepost flagengagement_scoreengagement_percentageTime Since PostPredicted post score
01i91b7mWhich type of onions do you like best when cooking indian food?https://www.reddit.com/r/IndianFood/comments/1i91b7m/which_type_of_onions_do_you_like_best_when/5krishnab752025-01-24 17:48:16False23m8yb17eMrMcgoomom13Red. Always. Thankfully they are now more widely available in the US. A few years ago I struggled to find red onions, cilantro and green chillies n grocery stores.2025-01-24 18:21:452025-01-24 17:48:16Only Text(No spam)39.50.3085940.07.045864
11i91b7mWhich type of onions do you like best when cooking indian food?https://www.reddit.com/r/IndianFood/comments/1i91b7m/which_type_of_onions_do_you_like_best_when/5krishnab752025-01-24 17:48:16False23m8yewgdcymshah3Depends on what I'm making. \nTomato-onion gravies (pav bhaji, chole chana, etc.) Use almost always red onions or whatever is on hand. \nFor biryani/pulao, shallots. \nIn salads, white or red onions. \nOnion pakora - red or large yellow onions2025-01-24 18:39:382025-01-24 17:48:16Only Text(No spam)39.50.3085940.07.045864
21i91b7mWhich type of onions do you like best when cooking indian food?https://www.reddit.com/r/IndianFood/comments/1i91b7m/which_type_of_onions_do_you_like_best_when/5krishnab752025-01-24 17:48:16False23m8y8ia0No-Record30076Also in America. Red onions. White ones don’t hold the flavour and nobody likes the white ones , even in salads.2025-01-24 18:10:012025-01-24 17:48:16Only Text(No spam)39.50.3085940.07.045864
31i91b7mWhich type of onions do you like best when cooking indian food?https://www.reddit.com/r/IndianFood/comments/1i91b7m/which_type_of_onions_do_you_like_best_when/5krishnab752025-01-24 17:48:16False23m8y5e3cshay77006I’m Gujarati in America. We use yellow onions in cooking and a sweet white onion for salads2025-01-24 17:55:362025-01-24 17:48:16Only Text(No spam)39.50.3085940.07.045864
41i91b7mWhich type of onions do you like best when cooking indian food?https://www.reddit.com/r/IndianFood/comments/1i91b7m/which_type_of_onions_do_you_like_best_when/5krishnab752025-01-24 17:48:16False23m8ycf4lMattSk872Small red onions or shallots. Red ones are much cheaper.2025-01-24 18:28:082025-01-24 17:48:16Only Text(No spam)39.50.3085940.07.045864
51i91b7mWhich type of onions do you like best when cooking indian food?https://www.reddit.com/r/IndianFood/comments/1i91b7m/which_type_of_onions_do_you_like_best_when/5krishnab752025-01-24 17:48:16False23m8yew7rSuitable_Secret55482Red onions, in Kerala . And shallots2025-01-24 18:39:372025-01-24 17:48:16Only Text(No spam)39.50.3085940.07.045864
61i91b7mWhich type of onions do you like best when cooking indian food?https://www.reddit.com/r/IndianFood/comments/1i91b7m/which_type_of_onions_do_you_like_best_when/5krishnab752025-01-24 17:48:16False23m8yhnyxMeeMawsBigToe2Always red2025-01-24 18:52:262025-01-24 17:48:16Too Short Message39.50.3085940.07.045864
71i91b7mWhich type of onions do you like best when cooking indian food?https://www.reddit.com/r/IndianFood/comments/1i91b7m/which_type_of_onions_do_you_like_best_when/5krishnab752025-01-24 17:48:16False23m8ym7vjEducational-Duck-9992Red is best, or Yellow if not available. I feel red onions are closer to onions in India2025-01-24 19:13:342025-01-24 17:48:16Only Text(No spam)39.50.3085940.07.045864
81i91b7mWhich type of onions do you like best when cooking indian food?https://www.reddit.com/r/IndianFood/comments/1i91b7m/which_type_of_onions_do_you_like_best_when/5krishnab752025-01-24 17:48:16False23m8y4dyqLate-Warning78495‘Onions’ in India are actually shallots2025-01-24 17:51:022025-01-24 17:48:16Only Text(No spam)39.50.3085940.07.045864
91i91b7mWhich type of onions do you like best when cooking indian food?https://www.reddit.com/r/IndianFood/comments/1i91b7m/which_type_of_onions_do_you_like_best_when/5krishnab752025-01-24 17:48:16False23m8ya0ioKrVrAr1Great question that I've wondered about as well. I'm in Spain and we get red and yellow onions. I usually use the white and occasionally red... I'm not sure if I can tell the difference but I also like spicy food so maybe that dominants the flavor? \n\nInterested to see what others have to say!2025-01-24 18:17:012025-01-24 17:48:16Only Text(No spam)39.50.3085940.07.045864
post idpost titlepost urlpost scoreposted bypost datensfwnumber of commentscomment idcomment authorcomment scorecomment bodycomment dateparsed post datepost flagengagement_scoreengagement_percentageTime Since PostPredicted post score
4411i4ulhzHow to consume amla without the sour taste?https://www.reddit.com/r/IndianFood/comments/1i4ulhz/how_to_consume_amla_without_the_sour_taste/8cool_cat15492025-01-19 09:11:20False44m7zoupjcool_cat15491Okay, I've never heard of this before\nI'll check it out !2025-01-19 15:15:082025-01-19 09:11:20Only Text(No spam)74.00.578125128.61555610.080316
4421i4ulhzHow to consume amla without the sour taste?https://www.reddit.com/r/IndianFood/comments/1i4ulhz/how_to_consume_amla_without_the_sour_taste/8cool_cat15492025-01-19 09:11:20False44m7zp8mwcool_cat15491What to have the chutney with?2025-01-19 15:17:062025-01-19 09:11:20Only Text(No spam)74.00.578125128.61555610.080316
4431i4ulhzHow to consume amla without the sour taste?https://www.reddit.com/r/IndianFood/comments/1i4ulhz/how_to_consume_amla_without_the_sour_taste/8cool_cat15492025-01-19 09:11:20False44m7zo0l8thecutegirl062Vit C supplements will be fine2025-01-19 15:10:492025-01-19 09:11:20Only Text(No spam)74.00.578125128.61555610.080316
4441i4ulhzHow to consume amla without the sour taste?https://www.reddit.com/r/IndianFood/comments/1i4ulhz/how_to_consume_amla_without_the_sour_taste/8cool_cat15492025-01-19 09:11:20False44m86bcdhlostlamb77881Capsule2025-01-20 15:42:342025-01-19 09:11:20Too Short Message74.00.578125128.61555610.080316
4451i4ulhzHow to consume amla without the sour taste?https://www.reddit.com/r/IndianFood/comments/1i4ulhz/how_to_consume_amla_without_the_sour_taste/8cool_cat15492025-01-19 09:11:20False44m812xkvlittlecloudberry1You’re welcome. Best of luck with your health!2025-01-19 19:12:122025-01-19 09:11:20Only Text(No spam)74.00.578125128.61555610.080316
4461i4ulhzHow to consume amla without the sour taste?https://www.reddit.com/r/IndianFood/comments/1i4ulhz/how_to_consume_amla_without_the_sour_taste/8cool_cat15492025-01-19 09:11:20False44m80x2voskin_bee3Yeah vit C is heat sensitive. And also a lot of antioxidants lose their potency upon being heated. \nWell if you absolutely don't like the taste then its okay. You can eat steamed amlas.. atleast something is better than nothing right. \nTry to mix and match (somedays raw somedays steamed) so you can get maximum possible benefits.2025-01-19 18:44:422025-01-19 09:11:20Only Text(No spam)74.00.578125128.61555610.080316
4471i4ulhzHow to consume amla without the sour taste?https://www.reddit.com/r/IndianFood/comments/1i4ulhz/how_to_consume_amla_without_the_sour_taste/8cool_cat15492025-01-19 09:11:20False44m7zp1wcviolet57482I have an auntie who puts a few amlas in her smoothie drink. Never knew why she did it 😊2025-01-19 15:16:092025-01-19 09:11:20Only Text(No spam)74.00.578125128.61555610.080316
4481i4ulhzHow to consume amla without the sour taste?https://www.reddit.com/r/IndianFood/comments/1i4ulhz/how_to_consume_amla_without_the_sour_taste/8cool_cat15492025-01-19 09:11:20False44m8arm4fartsy_introvert0141Amla is really good for hairs. I ate 1 amla everyday for just 1 week and my hairfall drastically reduced.2025-01-21 04:50:252025-01-19 09:11:20Only Text(No spam)74.00.578125128.61555610.080316
4491i4ulhzHow to consume amla without the sour taste?https://www.reddit.com/r/IndianFood/comments/1i4ulhz/how_to_consume_amla_without_the_sour_taste/8cool_cat15492025-01-19 09:11:20False44m80fr25forelsketparadise11You chop up whatever veggie you want to use or and put it in an open container big enough for you to stir and take water out of it. Then you add three spoons of grinded mustard seeds and salt to taste and fill it up with water and let it sit for three days to ferment either in sunlight or just like that and you have your probiotic drink ready. Usually you make it in plain water without vegetables and use vada from dahi vada and have kanji vada pr it's made of black carrots in winter with beatroot root added to it but you can really use any vegetables or fruits that are good for your gut. \n\nI had found this amla and fresh turmeric one recently and it tasted great. For that use 4 Amla and two whole turmeric. You can add one or two chillies too2025-01-19 17:24:112025-01-19 09:11:20Only Text(No spam)74.00.578125128.61555610.080316
4501i4ulhzHow to consume amla without the sour taste?https://www.reddit.com/r/IndianFood/comments/1i4ulhz/how_to_consume_amla_without_the_sour_taste/8cool_cat15492025-01-19 09:11:20False44m847a8wwhatliesinameme1With paratha/ rice or as is; your wish2025-01-20 05:26:152025-01-19 09:11:20Only Text(No spam)74.00.578125128.61555610.080316